{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This allows multiple outputs from a single jupyter notebook cell:\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "pd.__version__  # for the record"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a name=\"addplot\"></a>\n",
    "# Adding plots to the basic mplfinance plot()\n",
    "\n",
    "Sometimes you may want to plot additional data within the same figure as the basic OHLC or Candlestick plot.  For example, you may want to add the results of a technical study, or some additional market data.  \n",
    "\n",
    "This is done by passing information into the call to `mplfinance.plot()` using the `addplot` (\"additional plot\") keyword.\n",
    "\n",
    "The `addplot` keyword expects a `dict`.  The dict must contain the key `'data':` whose value is the actual additional data to be plotted.  This additional data may be a `list`, `numpy.ndarray`, `pandas.Series`, or `pandas.DataFrame`.  If it is a DataFrame, then *all* columns in that dataframe will be plotted.  \n",
    "\n",
    "The rest of the `addplot` dict contains various keywords and their values that are used to configure the additional plot in relation to the basic OHLCV plot, as can be seen in the examples below.\n",
    "\n",
    "It is *strongly* recommended that the caller construct the `addplot` dict using the helper function `mplfinance.make_addplot()` (see examples below).  This helper function:\n",
    "- simplifies the syntax for specifying additional plots\n",
    "- ensures that the dict contains default values for all possible configurable keywords\n",
    "- does some basic checking on the keyword values to ensure they are of the correct types.\n",
    "\n",
    "The `addplot` keyword can also accept a `list` of `dict`s, as one possible way of plotting multiple additional data sets on top of the basic OHLCV data.  See specific details in the the examples below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## `addplot` examples:\n",
    "\n",
    "Let's start with an example data set that includes Bollinger Band data, in addition to the basic OHLCV data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(252, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>UpperB</th>\n",
       "      <th>LowerB</th>\n",
       "      <th>PercentB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>132.089996</td>\n",
       "      <td>134.100006</td>\n",
       "      <td>131.779999</td>\n",
       "      <td>133.919998</td>\n",
       "      <td>117.161659</td>\n",
       "      <td>202385700</td>\n",
       "      <td>132.373927</td>\n",
       "      <td>125.316073</td>\n",
       "      <td>1.219057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-05</th>\n",
       "      <td>133.779999</td>\n",
       "      <td>134.080002</td>\n",
       "      <td>133.389999</td>\n",
       "      <td>133.809998</td>\n",
       "      <td>117.065437</td>\n",
       "      <td>165936000</td>\n",
       "      <td>133.254297</td>\n",
       "      <td>124.912703</td>\n",
       "      <td>1.066618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-06</th>\n",
       "      <td>133.490005</td>\n",
       "      <td>134.139999</td>\n",
       "      <td>133.110001</td>\n",
       "      <td>133.970001</td>\n",
       "      <td>117.205429</td>\n",
       "      <td>143331600</td>\n",
       "      <td>134.040915</td>\n",
       "      <td>124.627085</td>\n",
       "      <td>0.992467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2011-07-01  132.089996  134.100006  131.779999  133.919998  117.161659   \n",
       "2011-07-05  133.779999  134.080002  133.389999  133.809998  117.065437   \n",
       "2011-07-06  133.490005  134.139999  133.110001  133.970001  117.205429   \n",
       "\n",
       "               Volume      UpperB      LowerB  PercentB  \n",
       "Date                                                     \n",
       "2011-07-01  202385700  132.373927  125.316073  1.219057  \n",
       "2011-07-05  165936000  133.254297  124.912703  1.066618  \n",
       "2011-07-06  143331600  134.040915  124.627085  0.992467  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>UpperB</th>\n",
       "      <th>LowerB</th>\n",
       "      <th>PercentB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-06-27</th>\n",
       "      <td>132.419998</td>\n",
       "      <td>133.429993</td>\n",
       "      <td>131.970001</td>\n",
       "      <td>133.169998</td>\n",
       "      <td>118.980804</td>\n",
       "      <td>108088000</td>\n",
       "      <td>136.447962</td>\n",
       "      <td>128.140042</td>\n",
       "      <td>0.605441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-28</th>\n",
       "      <td>132.289993</td>\n",
       "      <td>132.990005</td>\n",
       "      <td>131.279999</td>\n",
       "      <td>132.789993</td>\n",
       "      <td>118.641281</td>\n",
       "      <td>169242100</td>\n",
       "      <td>136.500761</td>\n",
       "      <td>128.219241</td>\n",
       "      <td>0.551922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-29</th>\n",
       "      <td>135.199997</td>\n",
       "      <td>136.270004</td>\n",
       "      <td>134.850006</td>\n",
       "      <td>136.100006</td>\n",
       "      <td>121.598610</td>\n",
       "      <td>212250900</td>\n",
       "      <td>136.721010</td>\n",
       "      <td>128.792993</td>\n",
       "      <td>0.921670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2012-06-27  132.419998  133.429993  131.970001  133.169998  118.980804   \n",
       "2012-06-28  132.289993  132.990005  131.279999  132.789993  118.641281   \n",
       "2012-06-29  135.199997  136.270004  134.850006  136.100006  121.598610   \n",
       "\n",
       "               Volume      UpperB      LowerB  PercentB  \n",
       "Date                                                     \n",
       "2012-06-27  108088000  136.447962  128.140042  0.605441  \n",
       "2012-06-28  169242100  136.500761  128.219241  0.551922  \n",
       "2012-06-29  212250900  136.721010  128.792993  0.921670  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/SPY_20110701_20120630_Bollinger.csv',index_col=0,parse_dates=True)\n",
    "#df = df.loc['2012-01-01':,:]\n",
    "df.shape\n",
    "df.head(3)\n",
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Using this dataframe, we can of course plot a basic ohlc or candlestick plot:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 748.8x538.2 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import mplfinance as mpf\n",
    "s = mpf.make_mpf_style(base_mpf_style='classic',mavcolors=['lime','b','r'])\n",
    "mpf.plot(df.iloc[15:75,:],type='candle',volume=True,figscale=1.3,mav=(7,11,15),style=s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-5-a6774c8535dd>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-5-a6774c8535dd>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    STOP HERE\u001b[0m\n\u001b[0m            ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "STOP HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Let's say we want to plot the Lower Bollinger band along with the basic OHLCV plot.  \n",
    "\n",
    "We use `make_addplot()` to create the addplot dict, and pass that into the plot() function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kk = 0\n",
    "for jj in range(len(df)):\n",
    "    kk += 1\n",
    "    #if kk%8==0 or kk%9==0 or kk%10 ==0:\n",
    "    if kk%18==0:\n",
    "        df.iloc[jj,:] = float('nan')\n",
    "    if kk == 18: kk = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(23)\n",
    "df.tail(23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "apdict = mpf.make_addplot(df.iloc[0:50,:]['LowerB'])\n",
    "\n",
    "mpf.plot(df.iloc[0:50,:],type='line',mav=(2,4,6),style='mike')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "When creating the `addplot` dict, we can specify that we want a scatter plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x414 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x414 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x414 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x414 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#apd = mpf.make_addplot(df['LowerB'],type='scatter')\n",
    "# mpf.plot(df,type='candle',style='yahoo',mav=(24))\n",
    "# mpf.plot(df,type='candle',style='yahoo',mav=(24),yscale='log')\n",
    "# mpf.plot(df,type='candle',style='yahoo',mav=(24),yscale='symlog')\n",
    "# mpf.plot(df,type='candle',style='yahoo',mav=(24),yscale='linear')\n",
    "\n",
    "mpf.plot(df,type='candle',mav=(24))\n",
    "mpf.plot(df,type='candle',mav=(24),yscale='log')\n",
    "mpf.plot(df,type='candle',mav=(24),yscale='symlog')\n",
    "mpf.plot(df,type='candle',mav=(24),yscale='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = dict(abc='aaa',xya=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'xya' in d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def testkw(value,kwargs):\n",
    "    print('value=',value)\n",
    "    print('kwargs=',kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = 'testing12'\n",
    "kwargs = 'kwargs' if '123' in v else None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kwargs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if kwargs is None:\n",
    "    print(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "The above example is a trivial use of a scatter plot, where the default line plot makes more sense.  \n",
    "\n",
    "A more helpful use of a scatter plot might be to highlight specific movements in the data.  For example, let's say we want to highlight whenever the \"Percent B\" Bollinger metric drops below zero.  To do this, let's first calculate a series that contains this information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percentB_belowzero(percentB,price):\n",
    "    import numpy as np\n",
    "    signal   = []\n",
    "    previous = -1.0\n",
    "    for date,value in percentB.iteritems():\n",
    "        if value < 0 and previous >= 0:\n",
    "            signal.append(price[date]*0.99)\n",
    "        else:\n",
    "            signal.append(np.nan)\n",
    "        previous = value\n",
    "    return signal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Take a small data set, and calculate a series that shows when the percentB falls below zero:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tdf = df.loc['05-10-2012':'06-07-2012',]  # Take a smaller data set so it's easier to see the scatter points\n",
    "\n",
    "signal = percentB_belowzero(tdf['PercentB'], tdf['Close'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Now plot the calculated information as an additional scatter plot on top of the the OHLC data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "apd = mpf.make_addplot(signal,type='scatter')\n",
    "\n",
    "mpf.plot(tdf,addplot=apd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "We can customize the marker size and shape, to make the scatter markers easier to see:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "apd = mpf.make_addplot(signal,type='scatter',markersize=200,marker='^')\n",
    "\n",
    "mpf.plot(tdf,addplot=apd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Plotting multiple additional data sets\n",
    "\n",
    "There are two ways to plot multiple additional data sets.\n",
    "\n",
    "- If the configuration is the same for all additional data sets, simply pass a `DataFrame` for the data.  All columns in the DataFrame will be plotted.\n",
    "\n",
    "- Alternatively you can create multiple `dict`s and pass a `list` of `dict`s to the `addplot` keyword\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Passing a DataFrame as the addplot data plots all columns in the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcdf = df[['LowerB','UpperB']]  # DataFrame with two columns\n",
    "apd  = mpf.make_addplot(tcdf)\n",
    "mpf.plot(df,addplot=apd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Setting `addplot=` a `list` of `dict`s is another to create multiple additional plots.<br>This method is necessary if the additional plots will have different configurations.  For example:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "First prepare the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percentB_aboveone(percentB,price):\n",
    "    import numpy as np\n",
    "    signal   = []\n",
    "    previous = 2\n",
    "    for date,value in percentB.iteritems():\n",
    "        if value > 1 and previous <= 1:\n",
    "            signal.append(price[date]*1.01)\n",
    "        else:\n",
    "            signal.append(np.nan)\n",
    "        previous = value\n",
    "    return signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "low_signal  = percentB_belowzero(df['PercentB'], df['Close']) \n",
    "high_signal = percentB_aboveone(df['PercentB'], df['Close'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Now create the additional plot `dict`s and plot the data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "apds = [ mpf.make_addplot(tcdf),\n",
    "         mpf.make_addplot(low_signal,type='scatter',markersize=200,marker='^'),\n",
    "         mpf.make_addplot(high_signal,type='scatter',markersize=200,marker='v'),\n",
    "       ]\n",
    "\n",
    "mpf.plot(df,addplot=apds,figscale=1.25,volume=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Plotting additional data on panel \"B\"\n",
    "\n",
    "---\n",
    "We refer to the Main, Upper panel as Panel \"A\" and the Lower panel as Panel \"B\".\n",
    "\n",
    "It is possible to plot the additional data on Panel \"B\" (where volume is usually plotted).\n",
    "In this example, as is typical in Bollinger Band Analysis, we plot `PercentB` in panel B:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "apds = [ mpf.make_addplot(tcdf),\n",
    "         mpf.make_addplot(low_signal,type='scatter',markersize=200,marker='^'),\n",
    "         mpf.make_addplot(high_signal,type='scatter',markersize=200,marker='v'),\n",
    "         mpf.make_addplot((df['PercentB']),panel='B',color='g')\n",
    "       ]\n",
    "\n",
    "mpf.plot(df,addplot=apds,figscale=1.3,volume=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Plotting additional data with a *secondary y-axis*\n",
    "\n",
    "---\n",
    "\n",
    "- Notice in the above plot, in the lower panel with the \"Volume\" bars, we see that \"Percent B\" line ***has its own y-axis on the right side.***\n",
    "\n",
    "\n",
    "- `mpf.make_addplot()` has a keyword argument called `secondary_y` which can have **three** possible values: **`True`**, **`False`**, and **`'auto'`**.\n",
    "  - The default value is `'auto'` which means if you don't specify `secondary_y`, or if you specify `secondary_y='auto'`, then `mpf.plot()` will attempt to decide whether a secondary y-axis is needed, by comparing the order of magnitude of the addplot data with the order of magnitude of the data that is already on the plot.\n",
    "  - If **`mpf.plot()`** gets it wrong, you can always override by setting **`secondary_y=True`** or **`secondary_y=False`**.\n",
    "  \n",
    "---\n",
    "\n",
    "- Below we see that `make_addplot()` also allows setting the `linestyle` for each additional plot.\n",
    "\n",
    "\n",
    "- **Notice also** that we pass an alternative \"mplfinance `style`\" to demonstrate that **if the `style` specifies that the *primary y-axis* should be on the right, then `mpf.plot()` knows to put any `secondary_y` axes on the left.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "apds = [ mpf.make_addplot(tcdf,linestyle='dashdot'),\n",
    "         mpf.make_addplot(low_signal,type='scatter',markersize=200,marker='^'),\n",
    "         mpf.make_addplot(high_signal,type='scatter',markersize=200,marker='v'),\n",
    "         mpf.make_addplot((df['PercentB']),panel='B',color='g',linestyle='dotted')\n",
    "       ]\n",
    "\n",
    "mpf.plot(df,addplot=apds,figscale=1.5,volume=True,style='starsandstripes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "\n",
    "* **Below** we demonstrate that the main (upper) panel can also have a `secondary_y` axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "# Change order of magnitude and range of low_signal, so that it will require a `secondary_y`:\n",
    "# note: this calculation has no financial meaning whatsoever; we are just generating some \n",
    "#       data to modify the order of magnitude and range, so as to be able to demonstrate \n",
    "#       secondary_y on the main panel.\n",
    "new_low_signal = [x*20.*math.sin(x) for x in low_signal] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "apds = [mpf.make_addplot(tcdf,linestyle='dashdot'),\n",
    "        mpf.make_addplot(new_low_signal,type='scatter',markersize=200,marker='^',secondary_y='auto'),\n",
    "        mpf.make_addplot(high_signal,type='scatter',markersize=200,marker='v',color='orange'),\n",
    "        mpf.make_addplot((df['PercentB']),panel='B',color='g',linestyle='dotted')\n",
    "       ]\n",
    "\n",
    "mpf.plot(df,addplot=apds,figscale=1.5,volume=True,style='sas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The same plot, with a style that puts the primary y-axis on the left:\n",
    "mpf.plot(df,addplot=apds,figscale=1.5,volume=True,style='default')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
